Inrix Inc.

Alec Barker

Oliver B Downs

Craig H Chapman

Robert Cahn

Christopher Laurence Scofield

Weimin Mark Wang

Scott R Love

Robert Frederick Leidle

Mitchel A Burns, Jr

Todd Asher

Wayne Stoppler

Mitchel A Burns

Jesse S Hersch

Te-Ming Huang

Valerie Raybold Yakich

Cory Bradshaw

James Allen England

Robert C Cahn

Techniques are described for assessing road traffic conditions in various ways based on obtained traffic-related data, such as data samples from vehicles and other mobile data sources traveling on the roads, as well as in some situations data from one or more other sources (such as physical sensors near to or embedded in the roads). The assessment of road traffic conditions based on obtained data samples may include various filtering and/or conditioning of the data samples, and various inferences and probabilistic determinations of traffic-related characteristics from the data samples. In some situations, the filtering of the data samples includes identifying data samples that are inaccurate or otherwise unrepresentative of actual traffic condition characteristics, such as data samples that are not of interest based at least in part on roads with which the data samples are associated and/or that otherwise reflect vehicle locations or activities that are not of interest.

Filtering road traffic condition data obtained from mobile data sources

One or more techniques and/or systems are provided for routing a user between events using an itinerary. An event source (e.g., electronic calendar) may be assessed to identify event information (e.g., event locations) for events (e.g., meetings). The event information may be evaluated to determine a schedule of events. Routes between events within the schedule may be identified to determine route information (e.g., predicted travel times) for the routes. The event information and the route information may be evaluated to generate an itinerary of events. In an example, a constraint may be identified (e.g., a traffic accident along a route). The constraint may be evaluated to determine an impact (e.g., a user arriving to the meeting event late) of the constraint on the itinerary. Responsive to the impact exceeding an adjustment threshold, the itinerary may be adjusted (e.g., modifying a route between one or more events to avoid the accident).

Itinerary generation and adjustment system

Techniques are described for generating and using information regarding road traffic in various ways, including by obtaining and analyzing road traffic information regarding actual behavior of drivers of vehicles on a network of roads. Obtained actual driver behavior information may in some situations be analyzed to identify decision point locations at which drivers face choices corresponding to possible alternative routes through the network of roads (e.g., intersections, highway exits and/or entrances, etc.), as well as to track the actual use by drivers of particular paths between particular decision points in order to determine preferred compound links between those decision point locations. The identified and determined information from the analysis may then be used in various manners, including in some situations to assist in determining particular recommended or preferred routes of vehicles through the network of roads based at least in part on actual driver behavior information.

Learning road navigation paths based on aggregate driver behavior

Techniques are described for generating and using information regarding road traffic in various ways, including by obtaining and analyzing road traffic information regarding actual behavior of drivers of vehicles on a network of roads. Obtained actual driver behavior information may in some situations be analyzed to determine actual delays for vehicles encountering various particular road features in the network of roads, such as for identified decision points at which drivers face choices corresponding to possible alternative routes through the network of roads (e.g., intersections, highway exits and/or entrances, etc.) and/or for other traffic flow impediments. The identified and determined information from the analysis may then be used in various manners, including in some situations to assist in determining particular recommended or preferred routes of vehicles through the network of roads based at least in part on actual driver behavior information.

Learning road feature delay times based on aggregate driver behavior

Techniques are described for determining and using information regarding expected road traffic flow conditions information for vehicles traveling on roads. The expected road traffic flow conditions for a particular portion of a road may be generated by combining historical representative information about road traffic flow conditions for that road portion with current information about actual traffic flow on or near that road portion. The combination may, for example, provide benefits for estimating expected traffic flow conditions information for roads with structural flow obstructions that cause reduced traffic flow at certain road locations and times&#x2014;for example, the expected traffic flow conditions information may be based at least in part on fitting or otherwise adapting partial actual traffic flow information about a vehicle's actual travel path to a historical travel profile for a road that includes representative traffic flow information for various combinations of road locations and time periods.

Predicting expected road traffic conditions based on historical and current data

Techniques are described for displaying or otherwise providing information to users regarding various types of road traffic condition information in various ways. The information may be provided, for example, as part of a user interface (or 'UI'), which may in some situations further include one or more types of user-selectable controls to allow a user to manipulate in various ways what road traffic condition information is displayed and/or how the information is displayed. A variety of types of road traffic condition information may be presented to users in various manners, including by presenting information on graphically displayed maps for geographic areas to indicate various information about road conditions in the geographic area. In addition, provided controls may allow users to select particular times, select particular routes, indicate to perform animation of various types of changing traffic conditions over a sequence of multiple successive times, etc.

Displaying road traffic condition information and user controls

Techniques are described for generating predictions of future traffic conditions at multiple future times, such as by using probabilistic techniques to assess various input data while repeatedly producing future time series predictions for each of numerous road segments (e.g., in a real-time manner based on changing current conditions for a network of roads in a given geographic area). In some situations, one or more predictive Bayesian models and corresponding decision trees are automatically created for use in generating the future traffic condition predictions for each geographic area of interest, such as based on observed historical traffic conditions for those geographic areas. Predicted future traffic condition information may then be used in a variety of ways to assist in travel and for other purposes, such as to plan optimal routes through a network of roads based on predictions about traffic conditions for the roads at multiple future times.

Dynamic time series prediction of traffic conditions

Dynamic time series prediction of future traffic conditions

Techniques are described for automatically detecting anomalous road traffic conditions and for providing information about the detected anomalies, such as for use in facilitating travel on roads of interest. Anomalous road traffic conditions may be identified using target traffic conditions for a particular road segment at a particular selected time, such as target traffic conditions that reflect actual traffic conditions for a current or past selected time, and/or target traffic conditions that reflect predicted future traffic conditions for a future selected time. Target traffic conditions may be compared to distinct expected road traffic conditions for a road segment at a selected time, with the expected conditions reflecting road traffic conditions that are typical or normal for the road segment at the selected time. Anomalous conditions may be identified based on sufficiently large differences from the expected conditions, and information about the anomalous conditions may be provided in various ways.

Detecting anomalous road traffic conditions

Techniques are described for assessing road traffic conditions in various ways based on obtained traffic-related data, such as data samples from road traffic sensors (e.g., physical sensors that are near or embedded in the roads) and/or from vehicles and other mobile data sources traveling on the roads. The assessment of road traffic conditions based on obtained sensor data readings and/or other data samples may include various filtering and/or conditioning of the data samples, and various inferences and probabilistic determinations of traffic-related characteristics of interest. Assessing obtained data may further include determining traffic conditions (e.g., traffic flow and/or average traffic speed) for various portions of a road network in a particular geographic area, based at least in part on obtained data samples.

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Woodinville, WA, United States of America

Alec Barker